| Title | Virus Host Cell Genetic Material Transport: Computational ODE/PDE Modeling with R |
| Author(s) | William E. Schiesser |
| Publisher | Springer |
| Year | 2022 |
| Edition | 1st Edition |
| Language | English |
| Pages | 178 pages |
| ISBN | 9783031039739 |
| Genre / Domain | Science, Mathematics, Biology, Virology |
| Series | Unknown |
| Size | 8.6 MB |
| Extension |
Summary
Virus Host Cell Genetic Material Transport: Computational ODE/PDE Modeling with R, authored by William E. Schiesser, presents a focused and practical approach to modeling the spatiotemporal dynamics of viral genetic material (VGM) within host cells. Published in 2022, this book addresses the critical need for computational tools to simulate the complex processes of viral reproduction and dissemination at the cellular level. The book is built around a system of ordinary and partial differential equations (ODE/PDEs) that describe the transport, replication, and mutation of viral genetic material from the moment of attachment to a host cell.
The modeling framework is meticulously constructed, representing the movement of viral proteins across the cell membrane as a diffusion process governed by a PDE (Fick's second law), while the intracellular time evolution of the VGM is captured using ODEs. The book minimizes formal mathematical exposition, instead focusing on detailed, executable examples that can be run on standard computers. The numerical integration of these ODE/PDEs is performed using routines written in R, an open-source scientific computing system, making the modeling accessible to a wide audience. The book guides readers through the setup of initial conditions, which start from zero and deviate in response to the concentration of viral proteins at the outer membrane, providing a realistic simulation of the infection process.
The practical utility of this book lies in its hands-on approach. All the R routines developed for the models are available for download via a provided link, allowing readers to run the examples immediately without needing prior expertise in numerical methods or coding. The book demonstrates how to visualize the ODE/PDE dependent variables using basic R plotting utilities, offering immediate feedback on the simulation results. Furthermore, the routines are designed to be easily adapted for model variations, such as altering parameters or modifying the equation structure, enabling readers to explore different scenarios and extend the modeling to specific viruses or experimental conditions.
This book is targeted at computational biologists, mathematical modelers, virologists, and researchers in related fields who are interested in understanding the dynamics of virus-host cell interactions. It is also suitable for graduate students in bioinformatics, applied mathematics, and systems biology who wish to develop their skills in computational modeling. The accessible presentation style and the availability of ready-to-run code make it an excellent resource for those new to ODE/PDE modeling, as well as for experienced researchers seeking a reliable framework for studying viral kinetics.
As a contribution to the field of computational virology, this book provides a robust and reproducible platform for investigating viral replication and mutation dynamics. By emphasizing open-source tools and practical examples, William E. Schiesser makes advanced modeling techniques accessible to a broader scientific community. The book's publication by Springer ensures a high standard of scientific rigor, and its focus on a concrete biological problem with practical computational solutions makes it a valuable addition to the literature on mathematical biology and viral dynamics.
Key Features
- Presents a computational ODE/PDE modeling framework for viral genetic material transport in host cells.
- Uses the open-source R programming language for numerical integration and visualization.
- Provides a step-by-step, example-driven approach with minimal formal mathematics.
- Includes downloadable R routines that allow readers to run and adapt the models.
- Models both the diffusion of viral proteins across the cell membrane and intracellular genetic material evolution.
- Employs Fick's second law to represent the diffusion process with a PDE.
- Uses ODEs to model the time evolution of viral genetic material inside the cell.
- Begins simulations from zero initial conditions, reflecting the onset of infection.
- Demonstrates how to visualize results using basic R plotting utilities.
- Designed to be adaptable for variations and extensions of the ODE/PDE model.
- Requires no prior knowledge of numerical methods or coding to run the examples.
- Written by an expert in computational modeling and applied mathematics.
- Published by Springer, ensuring authoritative and high-quality scientific content.
About the Author
William E. Schiesser is the R. L. McCann Professor of Chemical Engineering and Professor of Mathematics at Lehigh University. He has over four decades of experience in numerical methods and computational science, with a particular focus on the solution of ordinary and partial differential equations. He is the author of numerous books and publications on computational mathematics, modeling, and simulation.
His research interests include the development and application of numerical methods for differential equations in chemical engineering, biomedical engineering, and environmental science. He has also been a visiting scholar at various institutions and has contributed to the advancement of computational tools for scientific research.
Related Books
- Computational Biology and Bioinformatics: A Practical Guide — K. R. Pardasani, R. R. P. K. S. Rao
- Modeling and Simulation of Biological Systems — S. S. R. S. S. S. R. Rao
- Differential Equations in Biology — William E. Schiesser
- Ordinary and Partial Differential Equations: With Special Functions, Fourier Series, and Boundary Value Problems — Ravi P. Agarwal, Donal O'Regan
- Mathematical Biology: I. An Introduction — James D. Murray
- Viral Pathogenesis and Immunity — Neal Nathanson
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Frequently Asked Questions
Q : What is the main focus of "Virus Host Cell Genetic Material Transport"?
R : The book focuses on developing and applying computational models to simulate the spatiotemporal dynamics of viral genetic material transport within host cells. It uses a system of ordinary and partial differential equations (ODE/PDEs) implemented in the R programming language.
Q : Who is the author and what are his credentials?
R : The author is William E. Schiesser, the R. L. McCann Professor of Chemical Engineering and Professor of Mathematics at Lehigh University. He is a leading expert in numerical methods and computational modeling with decades of experience.
Q : What mathematical methods are used in the book?
R : The book uses a combination of ordinary differential equations (ODEs) to model intracellular processes and partial differential equations (PDEs) to model the diffusion of viral proteins across the cell membrane. Numerical integration of these equations is performed using R.
Q : Is prior experience with R or numerical methods required to use this book?
R : No, the book is designed to be accessible to readers without prior experience. It provides ready-to-run R routines that can be downloaded and executed, with detailed explanations of the model setup and results.
Q : What is the significance of the ODE/PDE modeling approach presented?
R : This approach allows for the simulation of both the spatial and temporal aspects of viral infection at the cellular level. It provides a framework for understanding how viral genetic material spreads and mutates, which is critical for studying viral pathogenesis and drug development.
Q : What is the format and publication year of this book?
R : This is the first edition, published in 2022 by Springer. The book is available as a hardcover and eBook.
Q : How can the modeling routines be adapted for different viruses or scenarios?
R : The book explains that the R routines are designed to be easily adapted. Users can modify parameters, change the model equations, or extend the framework to represent different viral characteristics or experimental conditions.
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